World Academy of Science, Engineering and Technology 78 2011 Diagnosing the Cause and Its Timing of Changes in Multivariate Process Mean Vector from Quality Control Charts Using Artificial Neural Network Farzaneh Ahmadzadeh.

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Diagnosing the Cause and Its Timing of Changes in Multivariate Process Mean Vector from Quality Control Charts Using Artificial Neural Network

Farzaneh Ahmadzadeh

Abstract Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

It has been proven that quality control charts are very effective in detecting out of control signals. If a control chart signals a change in the process parameter, examining the process for special causes only at the time of the signal may be ineffective. Identifying the time of the parameter change will substantially assist the signal diagnostics procedure since it makes the search for special causes more efficient and corrective measures can be implemented sooner. According to the following literature review, it is well known that when a control chart signals an out of control condition, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time.

But, when several characteristics of a manufactured component are to be monitored simultaneously identifying the change point by itself would not effectively lead to the source of disturbance. In other words, in multivariate environment, effective root cause analysis requires not only the identification of the change point but also the knowledge on the variable(s) leading to the change in the process parameters. Nedumaran et al. [8] referred to or discussed? issue of change point identification for 2 control chart, when several quality characteristics are to be monitored simultaneously. They used maximum likelihood estimator to estimate a step change shift in a mean vector when observations follow a multivariate normal distribution. Several authors including Montgomery [9], Wade and Woodall [10], Hawkins [11], Hayter and Tusi [12], Kourti and MacGregor [13], Nottingham et al. [14], Niaki and Abbasi [15], Guh [16], and Hawnrg [17],[18],[19] have investigated issues related to the diagnostic analysis in multivariate environment. Bersimis et al. [20] provides a comprehensive literature review on the multivariate control charts along with different diagnostic analyses to identify variable(s) associated with the out of control condition.

This paper focuses on MEWMA charts and how to identify out of control signals. It describes how neural network may be used to identify the step change point in the process mean vector. Next section describes MEWMA procedure and section three provides ANN architecture to detect the change point, and identify which parameters have caused the change and in both parts the performance of the approach is assessed by using Monte Carlo simulation, finally the conclusions are provided.

This work was supported by the Islamic Azad University , Karaj Branch.

F. Ahmadzadeh is with the Department of Industrial ngineering, Islamic

The MEWMA chart was introduced by Lowry, Woodall, Champ and Rigdon [21]. They suppose that we observe X1, X2 . . . in the univariate case i.e. when p = 1. The univariate EWMA chart is based on these values: